Assessment in the Context of Uncertainty Using the Script Concordance Test: More Meaning for Scores
Bibliographic record
Abstract
BACKGROUND: The Script Concordance Test (SCT) uses authentic, ill-defined clinical cases to compare medical learners' judgment skills with those of experienced physicians. SCT scores are meant to measure the degree of concordance between the performance of examinees and that of the reference panel. Raw test scores have meaning only if statistics (mean and standard deviation) describing the panel's performance are concurrently provided. PURPOSE: The purpose of this study is to suggest a method for reporting scores that standardizes panel mean and standard deviation, allowing examinees to immediately gauge their performance relative to panel members. METHODS: Based on a statistical method of standardization, a new method for computing SCT scores is described. According to this method, test raw scores are converted into a scale in which the panel mean is set as the value of reference, and the standard deviation of the panel serves as a yardstick by which examinee performance is measured. RESULTS: The effect of this transformation on four data sets obtained from SCTs in radio-oncology, surgery, neurology, and nursing is discussed. CONCLUSION: This transformation method proposes a common metric basis for reporting SCT scores and provides examinees with clear, interpretable insights into their performance relative to that of physicians of the field. We recommend reporting SCT scores with the mean and standard deviation of panel scores set at standard scores of 80 and 5, respectively. Beyond SCT, our transformation method may be generalizable to the scoring of other test formats in which the performance of examinees and those of a panel of reference undertaking the same cognitive tasks are compared.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.228 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".